TY - GEN
T1 - Applying classification to rainfall nowcasting with topographical awareness
AU - Gong, Yi Jhong
AU - Lin, Kai Hsiang
AU - Chang, Jui Hung
AU - Hwang, Ren Hung
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/6
Y1 - 2018/12/6
N2 - Rainfall nowcasting provides the estimations of rainfall condition, such as accumulated precipitation, probability of precipitation forecast, and rainfall intensity prediction. Although numerical weather prediction (NWP) can simulate the atmospheric conditions, limited by the computation performance and the initial field data, the NWP does not perform well in short-term forecasting. Since atmosphere environment is a complex non-linear system, we used the deep learning approach to learn and perform the rainfall nowcasting. In this paper, we used the classification model based on a residual network and added the "side path" to input the additional data which could assist our model in acquiring prior knowledge. For the experiment, we input the topographic data to help the model include topographical awareness. In our experiment, the model trained by the additional topographic data achieved the higher accuracy than the model lacking the topographical recognition.
AB - Rainfall nowcasting provides the estimations of rainfall condition, such as accumulated precipitation, probability of precipitation forecast, and rainfall intensity prediction. Although numerical weather prediction (NWP) can simulate the atmospheric conditions, limited by the computation performance and the initial field data, the NWP does not perform well in short-term forecasting. Since atmosphere environment is a complex non-linear system, we used the deep learning approach to learn and perform the rainfall nowcasting. In this paper, we used the classification model based on a residual network and added the "side path" to input the additional data which could assist our model in acquiring prior knowledge. For the experiment, we input the topographic data to help the model include topographical awareness. In our experiment, the model trained by the additional topographic data achieved the higher accuracy than the model lacking the topographical recognition.
KW - Classification model
KW - Deep learning
KW - Nowcasting
KW - Topographical factors
UR - http://www.scopus.com/inward/record.url?scp=85060386065&partnerID=8YFLogxK
U2 - 10.1109/IC3.2018.00018
DO - 10.1109/IC3.2018.00018
M3 - Conference contribution
AN - SCOPUS:85060386065
T3 - Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018
SP - 37
EP - 42
BT - Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Cognitive Cities Conference, IC3 2018
Y2 - 7 August 2018 through 9 August 2018
ER -